A novel sequencing-based vaginal health
assay combining self-sampling, HPV detection
and genotyping, STI detection, and vaginal
microbiome analysis
Elisabeth M. BikID1‡, Sara W. Bird1‡, Juan P. Bustamante1, Luis E. Leon1, Pamela A. Nieto1, Kwasi Addae1, Vı´ctor Alegrı´a-Mera1, Cristian Bravo1, Denisse Bravo1, Juan
P. Cardenas1, Glenn A. Carson1, Adam Caughey1, Paulo C. Covarrubias1, Jose´ Pe´rez-Donoso1, Graham Gass1, Sarah L. Gupta1, Kira Harman1, Donna Marie B. Hongo1, Juan
C. Jime´nez1, Laurens Kraal1, Felipe Melis-Arcos1, Eduardo H. Morales1, Amanda Morton1, Camila F. Navas1, Harold Nuñez1, Eduardo Olivares1, Nicola´s O´ rdenes-Aenishanslins1,
Francisco J. Ossandon1, Richard Phan1, Raul Pino1, Katia Soto-Liebe1, Ignacio Varas1, Patricia Vera-Wolf1, Nathaniel A. Walton1, Daniel E. Almonacid1, Audrey D. Goddard1,
Juan A. Ugalde1, Susan Zneimer1, Jessica Richman1, Zachary S. Apte1,2*
1 uBiome, San Francisco, CA, United States of America, 2 Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, United States of America
‡ These authors are co-first authors on this work.
Abstract
The composition of the vaginal microbiome, including both the presence of pathogens involved in sexually transmitted infections (STI) as well as commensal microbiota, has been shown to have important associations for a woman’s reproductive and general health. Cur-rently, healthcare providers cannot offer comprehensive vaginal microbiome screening, but are limited to the detection of individual pathogens, such as high-risk human papillomavirus (hrHPV), the predominant cause of cervical cancer. There is no single test on the market that combines HPV, STI, and microbiome screening. Here, we describe a novel inclusive vaginal health assay that combines self-sampling with sequencing-based HPV detection and genotyping, vaginal microbiome analysis, and STI-associated pathogen detection. The assay includes genotyping and detection of 14 hrHPV types, 5 low-risk HPV types (lrHPV), as well as the relative abundance of 31 bacterial taxa of clinical importance, including Lacto-bacillus, Sneathia, Gardnerella, and 3 pathogens involved in STI, with high sensitivity, speci-ficity, and reproducibility. For each of these taxa, reference ranges were determined in a group of 50 self-reported healthy women. The HPV sequencing portion of the test was eval-uated against the digene High-Risk HPV HC2 DNA test. For hrHPV genotyping, agreement was 95.3% with a kappa of 0.804 (601 samples); after removal of samples in which the digene hrHPV probe showed cross-reactivity with lrHPV types, the sensitivity and specificity of the hrHPV genotyping assay were 94.5% and 96.6%, respectively, with a kappa of 0.841. For lrHPV genotyping, agreement was 93.9% with a kappa of 0.788 (148 samples), while sensitivity and specificity were 100% and 92.9%, respectively. This novel assay could be used to complement conventional cervical cancer screening, because its self-sampling a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS
Citation: Bik EM, Bird SW, Bustamante JP, Leon
LE, Nieto PA, Addae K, et al. (2019) A novel sequencing-based vaginal health assay combining self-sampling, HPV detection and genotyping, STI detection, and vaginal microbiome analysis. PLoS ONE 14(5): e0215945.https://doi.org/10.1371/ journal.pone.0215945
Editor: Maria Lina Tornesello, Istituto Nazionale
Tumori IRCCS Fondazione Pascale, ITALY
Received: May 3, 2018
Accepted: April 12, 2019
Published: May 1, 2019
Copyright:© 2019 Bik et al. This is an open access article distributed under the terms of theCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement: All bacterial and HPV
sequence data from human samples as well as pools of human samples are available at the European Nucleotide Archive database under accession number PRJEB25853.
Funding: The funder, uBiome, Inc. provided
support in the form of salaries for all authors, and decided which initial targets to include in the study design, but did not have any additional role in data collection and analysis, decision to publish, or
format can expand access among women who would otherwise not participate, and because of its additional information about the composition of the vaginal microbiome and the presence of pathogens.
Introduction
A woman’s vaginal health is critical for her general well-being and reproductive success, and is in part determined by the vaginal microbiome composition, the presence of pathogens associ-ated with sexually transmitted infections (STI), and the presence of human papillomavirus (HPV) types that can cause genital warts or cervical cancer. Current clinical vaginal health assays focus on the detection of STI or that of HPV, but there is no single test that combines these targets with vaginal microbiome analysis or with self-sampling.
The human vaginal microbiome has a unique composition compared to other microbial communities in the human body. In most healthy women, the vaginal microbiome is charac-terized by a low bacterial diversity and a dominance of lactobacilli, with low abundance of other bacterial genera [1,2]. Several vaginal microbial community types have been described, most of which are dominated by a singleLactobacillus species. Lactic acid produced by
lactoba-cilli lowers the vaginal pH, which is believed to create an environment unfavorable for the growth of pathogenic bacteria [3]. Low numbers of vaginal lactobacilli have been associated with many health conditions, such as bacterial vaginosis [1,4–6], aerobic vaginitis [7], cervicitis [8], and STI [9–12]. The composition of a woman’s vaginal microbiome thus plays an impor-tant role in women’s health and reproductive success. Yet, the analysis of this microbial com-munity is not part of regular health care for women. In the US, as in many other countries, most healthcare providers instead focus on the detection of high-risk human papillomavirus (hrHPV), the predominant cause for cervical cancer.
Cervical cancer is one of the major causes of cancer-related deaths in women, with an annual worldwide mortality of 250,000 [13,14]. hrHPV DNA can be detected in almost all (>99%) cervical cancer specimens, and HPV is therefore considered the predominant causa-tive agent for cervical cancer [15,16]. Although HPV infection is the most common STI world-wide, not all HPV infections will lead to cancer. Firstly, certain HPV types have higher oncogenic risks than others. Of the over 170 different HPV genotypes known to date, twelve types have been classified as Group 1 human carcinogens; these include types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, and 59 [17,18]. Together with other closely related HPV types, such as 66 and 68, which have been listed as probably or possibly carcinogenic, these are collectively called hrHPV types. hrHPV types 16 and 18 can be found in over 70% of cervical cancers [19] and the presence of these types is associated with the highest chance of developing cancer within 10 years [20]. However, other hrHPV genotypes have also been shown to cause cervical cancer, and especially among women of non-European descent [21]. In addition, many hrHPV infections are temporary and will be cleared within months of acquisition, without proceeding to pre-cancerous lesions [22]. Other HPV types, collectively called low-risk HPV (lrHPV), are not implicated in cervical cancer, but instead cause genital warts [23].
National cervical cancer screening programs are offered to women worldwide with a start-ing age between 20 and 35 years old [24]. Most of these programs involve an invitation for a Pap smear, in which a woman’s cervical cells are obtained by a physician for cytology [25], but additional molecular HPV testing is increasingly offered by health care providers as well [26]. In the United States, most healthcare providers follow the American College of Obstetricians
preparation of the manuscript. The specific roles of each author are articulated in the ‘author contributions’ section.
Competing interests: All of the authors of the
paper are current or past employees of uBiome, Inc. and have received stock options as well as other compensation. Some authors have patents pending in relation to this work: US Application No 15/198,818, Method and system for diagnostic testing, Application No 16/084,945, Method and system for microbiome-derived diagnostics and therapeutics for bacterial vaginosis, and Application No 16/115,542, Method and system for
characterization for female reproductive system-related conditions associated with
microorganisms. The data in this article were used in the development of a commercially available test product developed and marketed by uBiome. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
and Gynecologists (ACOG) guidelines [27] or the U.S. Preventive Services Task Force (USPSTF) guidelines for women [28] to come in for a Pap smear, often with HPV testing, every three to five years, depending on age and risk factors. The most recent USPSTF guide-lines, released in August 2018, even include HPV testing alone as a recommended option for women over 30 [29].
Several commercial kits have FDA pre-market approval for the molecular detection of HPV [30]. In a 2014 meta-analysis of 36 studies, the Qiagendigene Hybrid Capture 2 (HC2) assay
was the most widely used [31]. In the HC2 assay, cervical specimens are denatured with sodium hydroxide, denatured viral DNA is hybridized with specific RNA probes, and RNA: DNA hybrids are subsequently detected with antibodies [32]. The HC2 test detects 13 hrHPV types, but does not report which specific type is present. Other HPV detection assays involve the amplification of viral DNA by Polymerase Chain Reaction (PCR). The most widely used primer pairs for HPV PCR detection are the GP5+/6+ primers [33] and the degenerate MY09/ 11 primers [34] or the PGMY09/11 primer pool [35], which are all based on conserved regions in the viral L1 open reading frame. The COBAS 4800 assay detects 14 hrHPV types using mul-tiplex real-time PCR with specific probes; it reports the presence of HPV16, HPV18, or one of 12 remaining hrHPV types [36].
The vaginal microbiome is an emerging area of research in understanding the role of HPV infections and reducing the risk of cervical cancer [37]. Several studies suggest a relationship between the composition of the vaginal microbiota and the acquisition and persistence of HPV infection. For example, vaginal microbial diversity is increased during an HPV infection, with decreased levels ofLactobacillus species and an increased presence of other microbial
members such asSneathia species or Gardnerella vaginalis [38–42]. In addition, certain micro-biota compositions are associated with increased clearance of detectable HPV [40].
In this study, we tested the feasibility of a novel assay, that combines the detection and iden-tification of HPV DNA, STI-associated pathogens, and microbiome analysis on samples obtained through self-sampling. We validated the performance of marker gene amplification and sequencing to detect the presence and relative abundance of 31 clinically important bacte-rial targets with high precision and accuracy. In addition to detectingLactobacillus, Sneathia,
andGardnerella spp., this test detects STI-associated pathogens including Chlamydia tracho-matis, Mycoplasma genitalium, and Neisseria gonorrhoeae, which combined infect over 150
million people each year and cause genital tract infections and cervicitis [10–12]. The perfor-mance of a novel amplification and sequencing-based strategy for HPV detection and type-specific identification was compared to that of the most widely used test for HPV detection in cervicovaginal specimens, thedigene HC2 test. This assay was not developed to be employed
in a clinical setting, but intended to complement, rather than replace, current healthcare guidelines for in-clinic cervical cancer screening.
Materials and methods
Study participants and sample collection
This study was approved under a Human Subjects Protocol provided by an independent IRB (E&I Review Services, IRB Study #13044, 05/10/2013). E&I is fully accredited by the Associa-tion for the AccreditaAssocia-tion of Human Research ProtecAssocia-tion Programs, Inc. (AAHRPP), with Registration # IRB 00007807. uBiome undergoes a yearly voluntary continuing review by E&I Review Services to determine that the project meets the same scrutiny of human subjects pro-tections as projects conducted at research institutions. The IRB board membership of E&I Review Services is consistent with the Code of Federal Regulations (CFR) requirements of 21 CFR 56.107 and 45 CFR 46.107. Where required, specimens used in this study consisted of
vaginal samples from women who had signed an informed consent to have their samples used for research. All participants were 18 years or older.
A vaginal self-collection kit was sent to each participant’s home address, consisting of a ster-ile swab, a tube with sterster-ile water, a tube with zirconia beads in a proprietary lysis and stabiliza-tion buffer that preserves the DNA for transport at ambient temperatures, and sampling instructions (S1 Fig, included in the Supplementary Materials). Participants were instructed to wet the swab with the sterile water, insert the swab into the vagina as far as is comfortable, make circular movements around the swab’s axis for 1 minute (min), and then stir the swab for 1 min into the tube with lysis buffer and beads. After shaking the tube for 1 min to homog-enize, the tube was then shipped by the participants to the laboratory by regular mail.
For the determination of the reference ranges of the 31 bacterial targets, a set of 50 vaginal specimens, each from a different woman (average age 48.4± 15.6 years; range 23 to 79 years), was selected. Inclusion criteria were the following: completion of the voluntary health survey that every woman was invited to participate in, and no self-report of any of the following con-ditions: bacterial vaginosis, cervical cancer, genital herpes or warts, urinary tract infection, or infection with HPV,C. trachomatis, T. pallidum, or yeast. In addition, all of these women
reported no antibiotic usage in the six months before sampling.
A different set of specimens from 87 women was used to compare the performance of sam-pling with thedigene collection device (Qiagen, Gaithersburg, MD, USA) and DNA extracted
from samples collected with swabs. For this subset, women were asked to self-sample 2 vaginal specimens within 15 minutes. The first specimen was collected by using thedigene collection
device, which consists of a cervical brush and adigene transport tube with Specimen Transport
Medium (STM). The second specimen was collected using a pre-wetted swab and resuspended in a collection tube with lysis buffer and beads, as described above, and used for DNA
extraction.
For use in spiking and intra-run technical repeatability experiments described below, homogenized “vaginal pools” were created by combining 96 vaginal samples derived from 11 or 16 individuals who sampled themselves multiple times. These pools were created to make a relative large amount of a homogenized complex matrix similar to real biological samples. The pool created from samples taken by 16 subjects was used to test the synthetic DNA in spiking experiments, while the pool of samples taken by 11 subjects was used in the technical repeat-ability experiment (see below).
An additional set of 718 vaginal specimens were selected to compare the performance of thedigene HC2 HPV test using the hrHPV probe (601 specimens) and lrHPV probe (148
spec-imens; overlap of 31 specimens), respectively, versus that of the amplification and sequence-based HPV type identification described in this study. Of these, 361 samples were from sub-jects who consented, while the remaining samples were residual clinical samples that were ana-lyzed anonymously.
Positive STI control samples
Ten de-identified cervicovaginal swab specimens of known STI pathogen status were obtained through a commercial source (iSpecimen, Lexington, MA). Five of these samples were reported to be positive forC. trachomatis and negative for N. gonorrhoeae, while a second set
of five samples were negative forC. trachomatis and positive for N. gonorrhoeae. Each sample
was tested in five replicates for DNA extraction, 16S rRNA gene amplification, and target iden-tification as described below. To confirm the presence ofM. genitalium in 13 samples that
rRNA gene amplification described in this study, a PCR was performed using primers on the MgPa adhesin gene [43].
In silico 16S rRNA gene target performance metrics
The assay includes 31 bacterial targets with clinical relevance for women’s reproductive tract health, which were identified through an exhaustive literature search (Fig 1). The most relevant associations between health conditions and the vaginal microbiota were narrowed down by choosing associations with high statistical significance that were found in humans subjects, not in laboratory animals or bioreactors, but performed on case/control, cohorts or random-ized studied population. These include bacterial vaginosis [1,4–6], aerobic vaginitis [7], pelvic inflammatory disease [44], and sexually transmitted infections [10–12]. A complete list of these associations and references are provided inS1 Table. For each bacterial taxon intended to be included in this assay, using a process similar to that described in Almonacid et al. [45], we determinedin silico performance metrics for identification of each taxa (sensitivity,
speci-ficity, positive and negative predictive value). Briefly, sequences assigned to each taxon in the SILVA database (Version 123) [46] were considered to be real positives for that taxon. Then, assuming amplification with up to two mismatches with the primers used, we identified for each taxa the sequences that would produce an amplicon, and evaluated whether that amplicon is unique to the taxon of interest (ti) or also shared by sequences from different taxa (dt). The number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) was computed for different tolerance ratios for the quotient dt/ti, and subsequentlyin sil-ico performance metrics were assessed. Of the 72 bacterial targets initially selected, the 31
tar-gets selected for the assay had all fourin silico performance metrics above 90% (S2 Fig;S2 Table).
In silico HPV target performance metrics
In addition to the 31 bacterial targets, hrHPV and lrHPV targets were selected for inclusion in the assay, based on their published association with cervical cancer lesions or genital warts (Fig 1,S1 Table). HPV reference genomes were downloaded in August 2017 from the PaVE data-base, which is a repository of curated and annotated HPV genomes [47,48]. Only revised and recognized sequences (180 HPV genomes) were used for anin silico PCR amplification using a
set of 15 forward and 6 reverse primers (described below) targeting the L1 gene and allowing up to 4 mismatches between primers and target sequences. Under these conditions, the L1 genes from 118 HPV genomes could be amplifiedin silico. Of these, 19 HPV genomes,
includ-ing 14 hrHPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68) and 5 lrHPV types (6, 11, 42, 43, 44) were selected based on their association with health conditions according to lit-erature (S1 Table). In order to evaluate the performance metrics for identification of the HPV targets, sequences of the L1 segment of HPV genomes from the NCBI database were used. The search was filtered to sequences with length in the range 1,500–10,000 bp and with correct assignment of the type of the HPV (4177 sequences). These sequences were amplifiedin silico
using the primers described below. Following these steps, we generated 161,398 amplicons. These sequences were mapped using VSEARCH [49] at 95% of identity against an HPV ampli-con reference database ampli-consisting of the ampliampli-cons produced by the reference genomes in PaVE for the 19 HPV types included in our assay. The performance metrics were calculated as described above for the 16S rRNA gene targets. Briefly, the correct assignment of anin silico
NCBI amplicon against the reference was counted as a true positive and an incorrect assign-ment was considered as a false negative. Also, we considered as a false negative any genome from NCBI that our primers could not amplify. According to this, the 19 HPV types obtained
values for sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV) above 90% (S3 Table).
In vitro validation of bacterial targets
To test our ability to identify members of each of the 31 bacterial targets, synthetic DNAs (sDNAs or gBlocks, Integrated DNA Technologies, Inc., Coralville, IA), were designed encom-passing the V4 region of the 16S rRNA gene including primer regions, based on a SILVA rep-resentative sequence, plus 75 additional bases to both the 5’ and 3’ side, with one sDNA per target (S4 Table). The SILVA representative sequence per taxa was chosen by performing an all-against-all sequence comparison of all sequences in a taxa, and identifying as representative the sequence that shared the highest similarity with the largest number of sequences in the set.
To validate that each target could be detected in a vaginal swab specimen, 3 ng of each sDNA was spiked into 500μl aliquots of a vaginal pool, created by combining 96 vaginal speci-mens from 16 women included in this study, and DNA was extracted from each spiked vaginal pool (see below). Each spike-in experiment was performed in triplicate. Subsequently, bacterial targets were detected by amplification using PCR targeting the 16S rRNA gene, sequencing, and a bioinformatics pipeline described below. Each target included in the final panel was detected above limit of detection (LOD) (see below) in each of the triplicate spiked-in amplifi-cation reactions performed on the extracted DNA from the vaginal pool (not shown).
In addition, the LOD of each target in a complex background of other targets was deter-mined according to published guidelines [50]. First, to check for potential contamination, we calculated a limit of blank (LOB), which was calculated using a set of 77 blank wells of a 96-well PCR plate where wells of the first row and first column of the plate each contained 200 pg/μl of synthetic 16S rRNA gene DNA from different targets. The LOB was set as the average number of reads in these blank wells (18.57 reads) plus 1.65 standard deviations (29.70 reads), thus at 48.27 reads. To calculate the LOD of the bacterial target, pools of bacterial sDNAs were mixed in different ratios. To create these mixes, each bacterial sDNA was randomly assigned to one of two pools, A and B, that each contained sDNAs in equimolar amount. Each pool was serially diluted in PCR grade water. Pool A dilutions were mixed 1:1 with undiluted Pool B and vice versa. All pool A/B combinations were used in triplicate for DNA extraction, amplifi-cation, and sequencing as described below. For each target, the LOD was defined as the lowest concentration of sDNA where at least two of the three replicates contained at least 2 reads for that target in a sample with 10,000 reads or more. Using this LOD, we calculated a lower threshold for detection for each taxa at its LOD as the LOB (48.27) plus the standard deviation of the taxa at LOD�1.65. This threshold is used to correctly assign a taxa as identified in a sample at or above its LOD.
For targets that had both a species and a genus level sDNA present in the mixed pools A and B, a bioinformatic correction was applied. The total reads for a genus-level target for which a species within that genus was also present in the mixed pools, was defined as the total measured reads for the genus and subtracting all those reads corresponding to species-level targets belonging to that genus in the same pool mix, i.e., only reads that match to a genus and not to a species level were finally assigned to the genus.
Fig 1. The 31 bacterial targets and HPV targets covered by the assay and their associated health conditions. Dark blue dots indicate
targets positively associated with the conditions, while pink diamonds indicate inverse associations. SeeS1 Tablein the Supplementary Materials for more detailed information about e.g. the HPV genotypes included and a list of references.
In vitro validation of HPV targets
To test the ability of our assay to detect and genotype HPV targets, fragments of the L1 gene of approximately 600 bp long were ordered for each of the representative sequences of 19 HPV types in the PaVE database as sDNAs (gBlocks, Integrated DNA Technologies, Inc.). To repre-sent hrHPV type 68, two sDNAs were ordered, 68a and 68b. The sequences of the 20 gBlocks representing 19 HPV types (14 hrHPV and 5 lrHPV) are listed inS5 Table. To validate that each target could be detected in a vaginal swab specimen, 3 ng of each HPV sDNA was spiked into 500μl aliquots of a vaginal pool created by combining 96 vaginal specimens from 16 women included in this study, and DNA was extracted from each spiked vaginal pool (see below). Subsequently, the spiked HPV targets were detected by amplification using the PCR targeting the L1 gene and bioinformatics pipeline described below. Each spike-in experiment was performed in triplicate. Each HPV target was detected above the LOD (see below) in each of the triplicate spiked-in amplification reactions performed on the extracted DNA from the vaginal pool (not shown). Each target had a ratio > 0.1 for the number of HPV-assigned reads divided by the total number of normalized reads assigned to an internal spike-in control (see below).
To determine the LOD of HPV targets, 10-fold serial dilutions of the sDNAs representing HPV targets were made in nuclease-free water, ranging from 105to 102molecules perμl. Dilu-tions of one target were inversely combined with diluDilu-tions of another target, forming different pairs of HPV sDNAs. Each dilution pair was used directly as template for PCR in triplicate as described below.
DNA extraction and amplification targeting 16S rRNA and HPV L1 genes
DNA was extracted from vaginal specimens, pools thereof, or sDNA dilutions in tubes con-taining lysis/stabilization buffer as described previously [45]. For 16S rRNA gene amplifica-tion, extracted DNA was used as the input of a one-step PCR protocol to amplify the V4 variable region of the 16S rRNA gene. This PCR contained universal primers 515F and 806R [45,51], both with sample-specific indices and Illumina tags. PCR was performed as described before [45]. Following amplification, DNA was pooled by taking the same volume from each reaction.
For HPV amplification, extracted DNA was used as the input of a PCR protocol to amplify the HPV L1 gene. To each sample, sDNA with a randomized HPV type 16 sequence was added as an internal positive control. The first PCR mix contained a pool of previously described HPV specific primers [35,52], and two new primers, HPV_RSMY09-LvJJ_Forward: 5’ CGTCCTAAAGGGAATTGATC, and HPV_PGMY11-CvJJ_Reverse: 5’ CACAAGGCCAT AATAATGG. All these primers contained sequencing adaptor regions. The PCR products from the first amplification round were used as input for a second PCR containing sample-specific forward and reverse indices and Illumina tags. PCR products from this second step were pooled for sequencing.
The 16S rRNA gene and HPV PCR consolidated library pools were separately quantified by qPCR using the KAPA Library Quant Kit (Bio-Rad iCycler qPCR Mix) following the manufac-turer’s instructions using a BioRad MyiQ iCycler. Sequencing was performed in a paired-end modality on the Illumina NextSeq 500 platform rendering 2 x 150 bp paired-end sequences.
Sequence analysis and taxonomic annotation for bacterial targets
After sequencing, demultiplexing of reads according to sample-specific barcodes was per-formed using Illumina’s BCL2FASTQ algorithm. Reads were filtered using an average Q-score > 30. Forward and reverse 16S rRNA gene reads were appended together after removal
of primers and any leading bases, and clustered using version 2.1.5 of the Swarm algorithm [53] using a distance of one nucleotide and the “fastidious” and “usearch-abundance” flags. The most abundant sequence per cluster was considered the real biological sequence and was assigned the count of all reads in the cluster. The representative reads from all clusters were subjected to chimera removal using the VSEARCH algorithm [49]. Reads passing all above fil-ters (filtered reads) were aligned using 100% identity over 100% of the length against the true positive 16S rRNA gene sequences identifiedin silico from SILVA for each of the 31 taxonomic
groups targeted by the assay as described above (S4 Table). The relative abundance of each taxon was determined by dividing the count linked to that taxa by the total number of filtered reads.
Sequence analysis and taxonomic annotation for HPV targets
Raw sequencing reads were demultiplexed using BCL2FASTQ. Primers were removed using cutadapt [54]. Trimmomatic [55] was used to remove reads with a length less than 125 bp, and a mean quality score below 30. After that, forward and reverse paired reads were joined using custom in-house scripts and converted to a fasta file. Identical sequences were merged and written to a file in fasta format and sorted by decreasing abundance using—derep_fulllength option in VSEARCH [49]. Target sequences in the fasta files were compared to the fasta-for-matted query database sequences (19 HPV target sequences) using the global pairwise align-ment option with VSEARCH, using 95 percent sequence identity, to obtain the counts for each HPV type within a different sample.
The HPV portion of the assay was considered positive if the number of sequence reads assigned to the specific HPV types was above the threshold at the limit of detection, and greater than a previously defined cutoff. To set this cutoff, two normalization steps were employed. First, according toin silico PCR amplification, a different number of combinations
of primers amplify different HPV targets (e.g. HPV16 is amplified using 66 different combina-tions, while HPV43 is amplified with just 10 combinations), reflecting the sequence variability within the primer binding site among HPVs. This also means that the spiked-in internal con-trol and the target HPV have different amplification efficiencies. To avoid this bias, the inter-nal control (which has the primer sites for HPV16) is normalized for the amplification factor (number of primer combinations that generate an amplicon) of each HPV type. The number of HPV-assigned reads was divided by the total number of normalized reads assigned to the spike, and a sample was considered HPV-positive if that ratio was above 0.1, which corre-sponds to approximately 500 target molecules.
Intra- and inter-run precision
Intra-run technical repeatability was assessed by including nine replicates of the same vaginal pool (consisting of 96 vaginal samples derived from 11 individuals) into the same DNA extrac-tion, 16S rRNA gene amplificaextrac-tion, and sequencing run. This experiment was then repeated in two additional sequencing runs to yield three sets of nine replicate samples analyzed within the same run. In addition, inter-run technical reproducibility was performed by processing three replicates of a set of 18 vaginal samples on three different days by three different opera-tors. Samples included in the analysis were those that had at least 10,000 reads and where at least two of the three replicates were present (11 sets).
Comparison of the results, both intra- and inter-run, were done using the raw counts of the 31 bacterial species- and genus-level targets. Data was processed using the R-package Phyloseq [56], and visualized using Principal Coordinates Analysis (PCoA), based on a distance matrix calculated using the Bray-Curtis method.
digene HC2 hrHPV test on digene tubes or on extracted DNA
Thedigene HC2 HPV detection assay (Qiagen) was used as a reference to validate the hr- and
lrHPV portions of the assay. The High-Risk HPV Probe in thedigene HC2 HPV test detects
hrHPV types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68, while the Low-Risk HPV Probe in thedigene test detects lrHPV types 6, 11, 42, 43, and 44. The vaginal health test
described here detects all these types plus hrHPV type 66. Thedigene HC2 assay is intended to
be used directly on cervical samples collected in thedigene STM transport tube. In order to
val-idate the use of thedigene kit on extracted vaginal DNA, we first compared the performance of
thedigene HC2 assay on a set of 87 self-sampled, paired vaginal samples, i.e. a digene brush
resuspended in an STM transport tube, as well as DNA extracted from a vaginal swab resus-pended in lysis and stabilization buffer according to the manufacturer’s instructions. Negative and positive controls and calibrators included in the kit were processed within each 96-well assay, and used for assay validation and cutoff, as per instructions. A specimen was considered positive if its chemiluminescence measurement (Relative Light Units, RLU) was higher than or equal to that of the assay’s Positive Calibrator cutoff (RLU ratio of 1 or more), as specified in thedigene HC2 assay instructions.
Sensitivity, specificity, and accuracy of the hr- and lrHPV portions of the vaginal health assay were evaluated using thedigene HC2 HPV DNA assay as the gold standard, and
extracted DNA from 601 (hrHPV) and 148 (lrHPV) vaginal swabs, respectively, as the input for both tests. The vaginal health assay was considered to be positive for a HPV type if the number of reads assigned to that HPV type divided by the normalized number of reads assigned to a spiked-in control (see above) was greater than 0.1. Agreement between the two methods was evaluated using Cohen’s kappa [57], where the level of agreement is defined by the range: 0–0.20, poor; 0.21–0.40, fair; 0.41–0.60, moderate; 0.61–0.80, good; 0.81–1.00, very good.
Sequence availability
Bacterial sequence reads of the 31 bacterial taxa and 19 HPV strains targeted by our assay are available at the European Nucleotide Archive under accession number PRJEB25853 for all human samples as well as pools of human samples reported in this article.
Results
Limit of detection of bacterial and HPV targets
The vaginal health assay described here is based on a list of 31 bacterial 16S rRNA gene targets and 19 HPV types that were identified through an exhaustive literature search to play impor-tant roles in health and disease of women’s reproductive tracts (Fig 1,S1 Table). For each bac-terial target, the LOD was determined by combining different dilutions of pools of sDNAs, followed by DNA extraction, amplification of the V4 region of the 16S rRNA gene using broad-range primers, and sequencing (Fig 2A). The LOB was set as the average number of reads in 77 blank wells (18.57 reads) plus 1.65 standard deviations (29.70 reads). Using this value, we calculated the threshold of identification for each taxon as the LOB + 1.65 standard deviations (48.27) plus the standard deviation of the taxon at LOD�1.65. For the 31 taxa tar-geted by the assay, the threshold related to LODs was in the range 49.0 to 59.1reads (S6 Table).
To determine the LOD for the HPV targets, different dilutions of pools of sDNAs were mixed as done for the bacterial targets. The molecules were then amplified, sequenced, and analyzed by the HPV bioinformatics pipeline. For all HPV targets analyzed, the threshold related to LODs was in the range 40.8 to 224.8 reads (Fig 2B,S7 Table).
Intra- and inter-run variability
Intra-run technical variability was evaluated in a combined set of 18 replicates of the same vag-inal pool, each of which yielded 10,000 reads or more. Ordination plots of both genus and spe-cies level bacterial taxa (Fig 3) showed a tight clustering of intra-run technical replicates, indicating that within a single sequencing run, results generated by the laboratory process and the bioinformatics analysis were consistent.
For the inter-run analysis, a total set of 11 groups of replicates (at least two samples) passed the filtering criteria (over 10,000 reads). The PCoA visualization at genus and species level showed a dispersion of the different samples, but with a clustering according to the respective replicates (Fig 3). This suggests that there is limited within-sample variation when the same samples are processed on different days by different operators.
Relative abundance of bacterial targets in healthy vaginal samples
To determine reference ranges for the 31 bacterial targets in the assay, we selected a set of 50 vagi-nal samples from our database. These represent self-reported healthy individuals from the uBiome microbiome research study. In addition to health status, additional selection criteria included no usage of antibiotics six months prior, and no current urinary tract or vaginal infections, including the presence of STDs. The 50 samples were processed with the target database, and the relative abundance ranges for each bacterial target in the cohort are shown (Fig 4).
As expected, given the nature of the samples,Lactobacillus was the most abundant genus,
with the widest abundance distribution. At the species level, a similar distribution of the rela-tive abundances was found, including a wide range and a high relarela-tive abundance for Lactoba-cillus iners.
Pathogen detection
Among the 31 bacterial targets in the assay are three pathogens implicated in STI:C. trachoma-tis, N. gonorrhoeae, and M. genitalium. The performance of the assay to detect two of these Fig 2. Limit of detection (LOD) of sDNAs representing bacterial and viral targets in a complex background of other sDNAs. Dilutions of two pools of
sDNAs were mixed in different amounts, and microbial targets were amplified and sequenced. For each dilution and target, the relative abundance in samples with 10,000 reads or more are shown. A. LOD of bacterial targets. LOD read thresholds are provided inS6 Table. B. LOD of HPV targets. Two different sDNAs were used to represent hrHPV type 68. For each dilution and HPV type, the relative abundance in samples with 10,000 reads or more are shown. The LOD read thresholds for each HPV target are provided inS7 Table.
pathogens was confirmed on a set of ten clinical samples available through a commercial source, five of which were positive forC. trachomatis, and five of which were positive for N. gonorrhoeae. A vaginal pool consisting of samples derived from 11 healthy individuals was
included as a control sample, and was found to be negative (Fig 5).
The three STI-associated targets (C. trachomatis, M. genitalium, and N. gonorrhoeae) were
not present in any of the 50 samples from the healthy subject set (see also below), nor in a set of 87 vaginal samples used to validate the performance of thedigene test on extracted DNA
(see below). Twelve of thirteen positiveM. genitalium samples found in a larger set of samples
used to compare the HPV genotyping part of the assay (see below) were confirmed to be Fig 3. Inter- and intra-run reproducibility. PCoA ordination showing clustering of inter-run (11 vaginal samples analyzed in triplicate on three independent
sequencing runs) and intra-run (18 aliquots of the same vaginal pool) data, at the genus and species taxonomic level. Shapes indicate the sequencing run, while colors indicate sample replicates. For each category (Species and Genus) samples were processed together, sharing the same scale in the visualization.
positive in anM. genitalium specific adhesin PCR described by others [43], while eight out of nine negative samples were confirmed to be negative, corresponding to a sensitivity of 92.3% and a specificity of 88.9% (not shown).
Performance of
digene HC2 HPV test on extracted DNA
In order to validate the use of thedigene kit on extracted vaginal DNA, we compared the
per-formance of thedigene HC2 HPV assay on a set of 87 self-obtained, paired cervicovaginal Fig 4. Reference ranges for the bacterial targets in the assay. A set of 50 vaginal samples, each from a different woman, was selected based on the self-reported answers
given to survey questions indicating general and vaginal health. Each dot represents the relative abundance of a different bacterial target on genus level (top) or species level (bottom) within a different vaginal sample. Boxes indicate the 25th-76th percentile, with the median indicated inside each colored box. Red lines indicate the 99% percentile of each distribution, and are also the cutoff for the reference ranges. Not all of the taxa used in the assay were plotted, as some had no abundance values for this healthy cohort (Papillibacter, C. trachomatis, M. mulieris, M. genitalium, N. gonorrhoeae, P. amnii), based on the exclusion criteria.
https://doi.org/10.1371/journal.pone.0215945.g004
Fig 5. Experimental validation of 16S rRNA gene sequencing for pathogen detection using verification samples. Ten de-identified clinical verification specimens
(iSpecimen) containing eitherC. trachomatis (n = 5) or N. gonorrhoeae (n = 5), as well as a vaginal pool (VP) constructed by combining 96 vaginal samples from 11 individuals, were tested for the presence of either pathogen using 16S rRNA gene amplification and sequencing. Five replicates of each specimen were tested. The heatmap shows the relative abundance of the two pathogens in each replicate experiment, on a scale from light yellow (absent) to dark purple (100% relative abundance). https://doi.org/10.1371/journal.pone.0215945.g005
samples, i.e. adigene brush resuspended in digene STM, as well as DNA extracted from a
paired vaginal swab resuspended in lysis transport medium. Of the 87 samples, 84 showed con-cordant results (69 were negative and 15 were positive for HPV in both tests) (Table 1). Three sample pairs that were positive with thedigene STM sample were HPV negative when the
cor-responding test was performed on extracted DNA. These three samples had an averagedigene
RLU ratio of 1.94, suggesting that these contained low levels of HPV (Fig 6). Agreement betweendigene STM and digene DNA was of 96.6%, with a sensitivity of 83.3%, a specificity of
100%, and a Cohen’s Kappa of 0.89± 0.12 (Z = 8.39, p-value = 0.0001).
Performance of the hrHPV sequencing test on clinical samples
Using 601 vaginal specimens, the performance of the assay to detect hrHPV was compared to that of thedigene HC2 hrHPV assay. Of the 601 samples, 504 were negative in both tests, while
69 were positive in both tests (Table 2). Unexpectedly, three samples that were positive by sequencing for hrHPV 66, which is not covered by thedigene hrHPV probe, were still positive
in thedigene assay. Ten digene-positive samples did not yield any validated hrHPV reads after
amplification and sequencing by our assay. Of these ten false negatives, six samples were found to contain single or mixed non-high risk HPV strains by genotyping, including HPV 30, 61, 40, 42, 53, and 67. In addition, eighteen samples were negative in thedigene HC2 hrHPV assay
but yielded sufficient hrHPV reads (of types 16, 31, 35, 51, 52, 56, 59, and 68b) to be identified as positives by our genotyping assay. Thus, in comparison to thedigene HC2 hrHPV test, the
hrHPV sequencing assay had a sensitivity of 87.3% and a specificity of 96.6%, with an overall agreement of 95.3%, and a Cohen’s kappa of 0.804± 0.070 (Z = 19.8; p-value <0.0001). After removal of the six samples where cross-reactivity of the digene hrHPV probe with lrHPV sequences was suspected, the sensitivity and specificity of the hrHPV genotyping assay were 94.5% and 96.6%, respectively, with a kappa of 0.841± 0.065.
Good correlation was found between the number of normalized hrHPV sequencing reads and thedigene HC2 hrHPV RLU ratios, confirming that the PCR and sequencing hrHPV
assay described here can not only detect hrHPV types but also assess their relative abundance (Fig 7A).
Performance of the lrHPV sequencing test on clinical samples
Using 148 vaginal specimens, the performance of the assay to detect lrHPV by genotyping was compared to that of thedigene HC2 lrHPV assay. Of the 148 samples, 118 were negative in
both tests, while 21 were positive in both tests (Table 3). No false negatives were found, but nine samples that were negative in thedigene lrHPV assay were found to have lrHPV
sequences by genotyping, of types 42, 43, and 44. Using thedigene HC2 lrHPV test as the gold
standard, the lrHPV genotyping assay was found to have a sensitivity of 100% and a specificity of 92.9%, with an overall agreement of 93.9%, Cohen’s kappa = 0.788± 0.131 (Z = 9.81, p-value < 0.0001).
Table 1.digene HC2 High-Risk HPV assay performance on a set of 87 paired, self-collected vaginal samples. One set of samples was collected using adigene brush resuspended in digene Specimen Transport medium (“digene STM”), and the second set was extracted DNA from swabs suspended in tubes with lysis/stabilization buffer (“digene DNA”). Samples were considered to be HPV positive if the RLU ratio was 1 or more, as instructed in thedigene protocol.
digene STM hrHPV + digene STM hrHPV - Sum
digene DNA hrHPV + 15 0 15
digene DNA hrHPV - 3 69 72
Sum 18 69 87
As with the hrHPV assay, the number of normalized lrHPV sequencing reads was positively correlated to thedigene HC2 lrHPV RLU ratios, confirming that the PCR and sequencing
lrHPV assay described here can not only detect lrHPV types but also assess their relative abun-dance (Fig 7B).
Fig 6.digene HC2 High-Risk HPV assay performance on a set of 87 paired, self-collected cervicovaginal samples. Samples were tested directly from STM tubes or from a paired sample after DNA extraction. The purple lines show the cutoff of thedigene assay (RLU ratio = 1). Three STM specimens were positive for hrHPV with an average RLU ratio of <2 (low positive), but below RLU ratio = 1 for their corresponding extracted DNA specimen. The results for all other 84 specimens were concordant. TN, true negative; TP, true positive; FN, false negative; FP, false positive.
https://doi.org/10.1371/journal.pone.0215945.g006
Table 2. Comparison of the vaginal health assay for the detection of genotyped hrHPV in DNA extracted from 601 vaginal samples to that of thedigene HC2 hrHPV assay. The genotyping assay was considered positive if the normalized number of reads assigned to any of the validated hrHPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68) divided by the number of reads assigned to a spike in control was greater than 0.1. Thedigene test was con-sidered positive if the measured RLU was equal to or greater than the assay’s cutoff (RLU ratio of 1 or higher), as per the manufacturer’s instructions.
digene hrHPV + digene hrHPV - Sum
Genotyping hrHPV + 69 18 87 Genotyping hrHPV -10 504 514 Sum 79 522 601 https://doi.org/10.1371/journal.pone.0215945.t002
Positive clinical samples for hrHPV and lrHPV types
In the combined 718 samples used in the comparison of the vaginal health assay to thedigene
HC2 HPV assay, 142 samples were found to be positive for 1 or more of the 19 HPV types. Of these, 107 samples contained only a single HPV type, while 35 samples contained 2 or more HPV types. Among the 100 samples that contained at least one of the 14 validated hrHPV types, HPV types 52 (13%), 68 (13%), 59 (12%), and 16 (11%) were the most common. Within the 60 samples positive for at least one of the 5 validated lrHPV types, HPV type 42 (43%) and 6 (30%) were the most common, while type 11 was not found (Fig 8).
Discussion
Here, we describe a novel vaginal health assay combining vaginal microbiome analysis, STI-associated pathogen detection, and HPV detection and identification in a self-sampling for-mat. Although each of these components have been described before, to our knowledge, this Fig 7. Comparison of hrHPV and lrHPV amplification and genotyping to thedigene HC2 HPV assay. DNA from 718 vaginal samples was extracted and tested by PCR amplification and sequencing using HPV primers, and additionally used directly in thedigene assay using the HC2 hrHPV (panel A) or lrHPV (panel B) probe mix. For each sample, the x-axis shows the normalized ratio of reads assigned to validated HPV types over reads assigned to a spiked-in internal control, while the Y-axis shows thedigene HPV probe RLU values normalized over the assay’s cut-off RLU. The two purple lines show the cutoff for each of the assays. A. Comparison of hrHPV test results in a subset of 601 samples. Six samples that were positive in thedigene hrHPV assay and negative in the hrHPV genotyping assay, but in which lrHPV sequences were detected by genotyping, are shown as light purple triangles. B. Comparison of lrHPV test results in a subset of 148 samples.
https://doi.org/10.1371/journal.pone.0215945.g007
Table 3. Comparison of the vaginal health assay for the detection of lrHPV in DNA extracted from 148 vaginal samples by genotyping to that of thedigene HC2 lrHPV assay. The genotyping assay was considered positive if the normalized number of reads assigned to any of the validated lrHPV types (6, 11, 42, 43, 44) divided by the number of reads assigned to a spike in control was greater than 0.1. Thedigene test was considered positive if the measured RLU was equal to or greater than the assay’s cutoff (RLU ratio of 1 or higher), as per the manufacturer’s instructions.
digene lrHPV + digene lrHPV - Sum
Genotyping lrHPV + 21 9 30 Genotyping lrHPV -0 118 118 Sum 21 127 148 https://doi.org/10.1371/journal.pone.0215945.t003
assay is the first to combine all of these parts, thus offering women a unique opportunity to gain a broad perspective into their vaginal and reproductive health.
The detection of hrHPV in combination with vaginal self-sampling has been proposed as an effective method for cervical cancer risk screening [58]. Although the sensitivity of probe hybridization-based hrHPV detection, such as thedigene HC2 assay, in self-obtained vaginal
swabs has been found to be slightly lower than that in clinician-obtained cervical specimens, hrHPV detection based on PCR was shown to be equally sensitive in self-sampled specimens [31]. While the vaginal health assay described in this study is not intended to replace regular cervical cancer screening programs, offering women the opportunity to self-collect vaginal specimens poses fewer barriers for women to be screened, and thus could lead to increased participation rates [59,60]. Therefore, allowing self-collection of vaginal samples for hrHPV screening in parallel to regular screening programs, as already implemented in numerous countries, and recommending women to seek further physician examination in case of a posi-tive result may have a posiposi-tive impact on rates of detection of cervical cancer, and potentially save lives [58].
The vaginal health assay described here not only detects whether HPV is present in a sam-ple, but also identifies the presence of specific type(s) by using sequencing analysis. Several other HPV genotyping assays based on PCR and sequencing have been reported [30,61–65]. These studies detected HPV types not found by traditional methods [62], as well as infections with multiple types [63–65] with high sensitivity. As recommended by the VALGENT study framework [66], we compared the performance of the HPV component of the test to that of the widely useddigene HC2 HPV assay. Because the novel test reported here is performed on
extracted DNA, we first validated the use of thedigene assay on extracted DNA. The digene Fig 8. Distribution of the 19 HPV types in this study. A set of 718 vaginal samples was tested in the HPV genotyping assay described in
this study. Of these, 142 samples were positive for at least one of the 19 HPV types validated in this study, with 100 samples positive for hrHPV, and 60 for lrHPV. In 35 of the 142 positive samples, two or more HPV types were found. Positive samples are plotted as percentage found in the total set of 718 samples.
performance on the extracted DNA was slightly less sensitive than that directly performed on thedigene STM tubes. The digene HC2 assay has been found to give discordant results in
about 8% of paired tests [67,68], where, for example, a positive sample will test negative at retesting, most often in samples with a low RLU ratio in the positive test. A cutoff ratio of 2 or 3 instead of 1 has been proposed to serve as a better indicator for reproducible positive results [67–70]. In our study, all specimens with RLU ratios of 2 or higher in the directdigene HC2
test on STM tubes were also correctly identified as positive when the test was performed on extracted DNA, suggesting that thedigene assay can be applied to extracted DNA as well.
Using extracted DNA from 718 vaginal specimens as the template, the performance of the HPV genotyping parts of the vaginal health assay was compared to that of thedigene HC2
HPV assay. The hrHPV genotyping assay showed good correlation with thedigene assay, with
a sensitivity and specificity of 87.3% and 96.6%, respectively, and a Cohen’s kappa of 0.804. Three samples containing hrHPV type 66 were found to be positive in both tests, even though they are supposedly not covered by thedigene assay. Among a set of ten samples that were
reported positive by the hrHPVdigene assay but that did not contain hrHPV sequences as
determined by our assay, six samples were found to contain single or mixed lrHPV types, including HPV 30, 61, 40, 42, 53, and 67. Cross-reactivity of thedigene HC2 hrHPV probe mix
with lrHPV sequences has been demonstrated by several others [69,71–74]. Thus, even though these samples had to be classified as false-negative because thedigene HC2 HPV assay was
taken as the gold standard, it is likely they were actually false-positives in thedigene test.
Removing these six samples from the comparison, the sensitivity and specificity of the hrHPV genotyping assay were 94.5% and 96.6%, respectively, with a kappa of 0.841± 0.065. The lrHPV genotyping assay had a sensitivity of 100% and a specificity of 92.9%, with a Cohen’s kappa of 0.788. In addition, the two tests were in good general agreement about the relative amount of HPV molecules detected. The specific genotyping of HPV by sequencing therefore can detect the presence of lrHPV that are known to give false-positive results in conventional clinical hrHPV tests. In addition, HPV genotyping provides clinicians and patients with more detailed information about which strain(s) are present in the vagina, allowing for more precise tracking of infection and clearance than most conventional assays [30].
Among the samples tested positive with hrHPV genotyping, HPV types 16, 52, 59, and 68 were most common. HPV types 6 and 42 were the most prevalent lrHPV types in the samples tested in this study. Although our sample set was small (718 samples, of which 142 tested posi-tive) and only screened for the presence of 19 HPV types, the high proportion of positive sam-ples for types 6, 16, 52, 59 has been found by others [75,76], while the results for hrHPV type 68 appears to be unique in our dataset.
In addition to the HPV portion of the novel vaginal health assay described here, the assay also reports the relative abundance of commensal and pathogenic bacteria in vaginal samples, and compares these to reference ranges. Self-collection has been shown to be well-suited for vaginal microbiome analysis as reported by Forney et al. who showed that microbial diversity is similar between self-collected and physician collected vaginal samples [77].
Several bacteria have been associated with vaginal health conditions, such as bacterial vagi-nosis [1,4–6], aerobic vaginitis [7], pelvic inflammatory disease [44], and sexually transmitted infections [9–12]. The vaginal health assay described here detects the relative abundance of bacteria positively associated with bacterial vaginosis, such asSneathia or Gardnerella species,
as well as those negatively associated with that condition such asLactobacillus species. In
addi-tion, it detects the presence of three common STI-associated pathogens, i.e.,C. trachomatis, N. gonorrhoeae, and M. genitalium. Of these, M. genitalium has been recently recognized as an
important pathogen implicated in pelvic inflammatory disease and infertility [12,78]. Although some early diagnostic tests have been described [79,80], very few clinicians test for
its presence. Furthermore, the vaginal microbiota composition has been reported to be associ-ated with the progression of HPV infection, from early states to cervical cancer [37,40]. Vagi-nal microbiome aVagi-nalysis therefore not only can be used to detect STI-associated pathogens and bacteria involved in bacterial vaginosis, but also to assess a woman’s microbiome similar-ity to the microbiome of a group of individuals with progressed HPV infection. This brings about the opportunity to leverage microbiome information to understand HPV infection pro-gression and women’s susceptibility to cancer development. Future versions of this assay could also include additional microorganisms associated with pathogenic outcomes such as Tricho-monas vaginalis, Mycoplasma parvum, and Ureaplasma urealithicum.
In conclusion, we here present a vaginal health assay that for the first time combines the detection of the most important bacterial and viral indicators of vaginal health and disease. We envision that this test has the potential to provide clinicians and patients with a more com-prehensive understanding of the vaginal microbiome, and to encourage women to take an active role in related conversations with their doctors. We also hope that by improving accessi-bility through self-sampling, we may encourage more women to engage with current screening and treatment guidelines for vaginal pathogens and cervical cancer.
Supporting information
S1 Fig. Vaginal sampling instructions. Participants in this study were sent a vaginal sampling
kit containing a swab, sterile water to pre-wet the swab, a tube containing zirconia beads and a lysis and stabilization buffer, and sampling instructions such as the one shown above. After sampling according to the instructions, participants could ship their sample back by regular mail.
(PDF)
S2 Fig.In silico performance metrics of the bacterial targets. Initially, 72 bacterial targets were identified based on their association with vaginal and reproductive health, comprised of 57 species and 15 genera. The following performance metrics were evaluated based on the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) detected in a manually curated amplicon database (described in S1 Doc in Almonacid
et al., 2017). The target performance are plotted as follows: specificity = TN / (TN + FP);
sensitivity = TP / (TP + FN); positive predictive value (PPV) = TP / (TP + FP); and negative predictive value (NPV) = TN / (TN + FN). Based on a cutoff of 90% (red vertical line), 31/72 preliminary targets passed for each of the parameters, resulting in the accuratein silico
detec-tion of 16 bacterial species (light purple), and 15 bacterial genera (dark purple). (PDF)
S1 Table. Assay targets and associated health conditions. List of all 31 bacterial targets and
19 HPV targets, their associations with different health conditions, and references. (PDF)
S2 Table.In silico performance metrics for the 72 bacterial targets (genus and species
level) that were initially selected. PPV, positive predictive value; NPV, negative predictive
value. All values in the sensitivity, specificity, PPV, and NPV columns are given as percentages. Of the 72 selected targets, 31 passed selection criteria of all values above 90%. Values below 90% are shown in red.
(PDF)
S3 Table.In silico performance metrics for the 19 HPV targets. HPV, human papillomavi-rus; TP, true positive; FN, false negative; FP, false positive; TN, true negative; Sens, sensitivity
(in %); Spec, specificity (in %); PPV, positive predictive value (in %); NPV, negative predictive value (in %).
(PDF)
S4 Table. List of the 31 synthetic DNAs created to represent the bacterial targets included in the assay. Sequences are based on the 16S rRNA gene.
(PDF)
S5 Table. List of the 20 synthetic DNA sequences representing 5 lrHPV and 14 hrHPV types included in the assay. Because of sequence variability, hrHPV type 68 was represented
by 2 different sDNAs. (PDF)
S6 Table. Limit of detection (LOD) assay for the bacterial targets. The table shows the
low-est dilution at which at least two of the three replicates had 2 or more reads per taxon, and the calculated threshold for identification per taxa at the LOD in number of reads.
(PDF)
S7 Table. Limit of detection (LOD) assay for the HPV targets. The table shows the lowest
dilution at which at least two of the three replicates had 2 or more reads per HPV type, and the calculated threshold for identification per HPV type at the LOD in number of reads.
(PDF)
Author Contributions
Conceptualization: Elisabeth M. Bik, Sara W. Bird, Laurens Kraal, Felipe Melis-Arcos,
Natha-niel A. Walton, DaNatha-niel E. Almonacid, Audrey D. Goddard, Juan A. Ugalde, Jessica Rich-man, Zachary S. Apte.
Data curation: Elisabeth M. Bik, Sara W. Bird, Juan P. Bustamante, Luis E. Leon, Pamela A.
Nieto, Vı´ctor Alegrı´a-Mera, Juan P. Cardenas, Donna Marie B. Hongo, Laurens Kraal, Felipe Melis-Arcos, Camila F. Navas, Harold Nuñez, Francisco J. Ossandon, Raul Pino, Ignacio Varas, Patricia Vera-Wolf, Daniel E. Almonacid, Audrey D. Goddard, Juan A. Ugalde, Susan Zneimer.
Formal analysis: Elisabeth M. Bik, Sara W. Bird, Juan P. Bustamante, Luis E. Leon, Pamela A.
Nieto, Glenn A. Carson, Donna Marie B. Hongo, Francisco J. Ossandon, Audrey D. God-dard, Juan A. Ugalde, Susan Zneimer.
Funding acquisition: Nathaniel A. Walton, Jessica Richman, Zachary S. Apte. Investigation: Elisabeth M. Bik, Sarah L. Gupta.
Methodology: Elisabeth M. Bik, Juan P. Bustamante, Luis E. Leon, Pamela A. Nieto, Kwasi
Addae, Vı´ctor Alegrı´a-Mera, Cristian Bravo, Denisse Bravo, Juan P. Cardenas, Adam Caughey, Paulo C. Covarrubias, Jose´ Pe´rez-Donoso, Graham Gass, Kira Harman, Juan C. Jime´nez, Eduardo H. Morales, Amanda Morton, Camila F. Navas, Harold Nuñez, Eduardo Olivares, Nicola´s O´ rdenes-Aenishanslins, Francisco J. Ossandon, Richard Phan, Raul Pino, Ignacio Varas, Juan A. Ugalde, Susan Zneimer.
Project administration: Laurens Kraal, Katia Soto-Liebe. Resources: Jessica Richman, Zachary S. Apte.
Software: Juan P. Bustamante, Luis E. Leon, Vı´ctor Alegrı´a-Mera, Felipe Melis-Arcos, Camila
F. Navas, Harold Nuñez, Francisco J. Ossandon, Raul Pino, Ignacio Varas, Patricia Vera-Wolf.
Supervision: Sara W. Bird, Jose´ Pe´rez-Donoso, Donna Marie B. Hongo, Juan C. Jime´nez,
Eduardo H. Morales, Amanda Morton, Katia Soto-Liebe, Nathaniel A. Walton, Daniel E. Almonacid, Audrey D. Goddard, Susan Zneimer.
Validation: Elisabeth M. Bik, Sara W. Bird, Pamela A. Nieto, Kwasi Addae, Cristian Bravo,
Denisse Bravo, Glenn A. Carson, Adam Caughey, Paulo C. Covarrubias, Graham Gass, Sarah L. Gupta, Kira Harman, Donna Marie B. Hongo, Juan C. Jime´nez, Eduardo H. Mora-les, Amanda Morton, Eduardo Olivares, Nicola´s O´ rdenes-Aenishanslins, Richard Phan, Audrey D. Goddard, Juan A. Ugalde, Susan Zneimer.
Visualization: Elisabeth M. Bik, Pamela A. Nieto, Francisco J. Ossandon, Patricia Vera-Wolf,
Juan A. Ugalde.
Writing – original draft: Elisabeth M. Bik, Sara W. Bird, Pamela A. Nieto, Juan P. Cardenas,
Sarah L. Gupta, Daniel E. Almonacid.
Writing – review & editing: Elisabeth M. Bik, Sara W. Bird, Daniel E. Almonacid, Audrey D.
Goddard, Juan A. Ugalde, Jessica Richman, Zachary S. Apte.
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